Self-Explanation Prompting Improves Dialogue Understanding in Large Language Models
Haoyu Gao, Ting-En Lin, Hangyu Li, Min Yang, Yuchuan Wu, Wentao Ma, Fei Huang, Yongbin Li
Abstract
Task-oriented dialogue (TOD) systems facilitate users in executing various activities via multi-turn dialogues, but Large Language Models (LLMs) often struggle to comprehend these intricate contexts. In this study, we propose a novel “Self-Explanation” prompting strategy to enhance the comprehension abilities of LLMs in multi-turn dialogues. This task-agnostic approach requires the model to analyze each dialogue utterance before task execution, thereby improving performance across various dialogue-centric tasks. Experimental results from six benchmark datasets confirm that our method consistently outperforms other zero-shot prompts and matches or exceeds the efficacy of few-shot prompts, demonstrating its potential as a powerful tool in enhancing LLMs’ comprehension in complex dialogue tasks.
BibTeX
@inproceedings{gao-etal-2024-self,
title = "Self-Explanation Prompting Improves Dialogue Understanding in Large Language Models",
author = "Gao, Haoyu and
Lin, Ting-En and
Li, Hangyu and
Yang, Min and
Wu, Yuchuan and
Ma, Wentao and
Huang, Fei and
Li, Yongbin",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1269/",
pages = "14567--14578"
}